Survey of Event Extraction in Low-resource Scenarios
As one of the tasks of information extraction,event extraction aims to extract structured event information from un-structured text.The current automated information extraction methods based on machine learning and deep learning rely on la-beled data excessively,but standard datasets in most areas are small and unevenly distributed.So the low-resource scenarios be-come an important bottleneck that limits the performance of automated information extraction.Although in recent years,many scholars have conducted in-depth research on low resource scenarios and produced many remarkable results,there is still a lack of research on event extraction in this scenario at present.This paper makes a comprehensive summary and analysis of existing aca-demic achievements.Firstly,it introduces the definition of related task,and the task of event extraction in low resource scenarios is divided into three categories.Then six kinds of related techniques and methods are discussed around this classification,inclu-ding transfer learning based,prompt learning based,unsupervised learning based,weakly supervised learning based,data and au-xiliary knowledge enhancement based,and meta learning based approaches.Subsequently,the shortcomings of current methods and strategies for future improvement are pointed out.Then the related datasets and evaluation metrics are introduced and the ex-perimental results of typical techniques are summarized and analyzed.Finally,the challenges and future research trends about event extraction in low resource scenarios are summarized and analyzed from a global perspective.
万方数据
维普
